1. Field
The following description relates to image processing, and more particularly, to facial emotion recognition systems.
2. Description of the Related Art
The ability to interpret emotions is very important for effective communication. For ideal human computer interfaces (HCI), it is desired that machines have robust facial emotion recognition system. Emotion recognition from facial expressions is a well studied field that uses Facial Action Coding System (FACS). The FACS is a system to taxonomize human facial expressions. FACS human coders can manually code nearly any anatomically possible facial expression, further deconstructing it into the specific Action Units (AU) and their temporal segments that produced the expression. In order to build a robust facial emotion recognition system subtle expression, head pose, talking faces, illumination conditions, and occlusions have to be handled. Existing facial emotion recognition systems perform facial emotion recognition but have limitations and fall short while recognizing facial emotions with facial artifacts such as eyeglasses, facial hair, scars, birth marks, wrinkles, and so on.
Occlusions in the presence of accessories (glasses, beards, and so on) to some extent are inherently handled using fiducial point approaches where an emotion is concluded using geometric patterns from the eyes, eyebrows, and mouth region. The disadvantage of geometrical pattern technique is the sensitivity to scaling and rotation of a face in the image plane and therefore would not be robust. Facial variations, artifacts, or facial biases may cause the features that characterize a face to be distorted. It is desirable to identify problematic facial artifacts that may cause false identification or no identification. External facial variations may be due to illumination, head pose, scale, and translation, while internal facial variations may be due to hair color, hair style, makeup, moustache, beard, and eyeglasses, as well as facial variations which stem from the user itself. Facial artifacts in the vicinity of facial muscles, Action Units (AU), will result in an incorrect analysis of emotion.
Due to abovementioned reasons, existing systems fails to recognize facial emotions in the presence of facial artifacts or occlusions to provide a robust facial expression based emotion recognition system.